code: def testPrivateArgPreventsCholeskyRecomputation(self):
      x = np.random.uniform(-1, 1, (4, 7)).astype(np.float32)
      chol = np.eye(4).astype(np.float32)
      mock_cholesky_fn = mock.Mock(return_value=chol)
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic()
      k = schur_complement.SchurComplement.with_precomputed_divisor(
          base_kernel, x, cholesky_fn=mock_cholesky_fn
      )
      mock_cholesky_fn.assert_called_once()
  
      # Assert that the Cholesky is not recomputed when the kernel is rebuilt and
      # its methods are called.
      mock_cholesky_fn.reset_mock()
      k2 = schur_complement.SchurComplement.with_precomputed_divisor(
          base_kernel,
          x,
          cholesky_fn=mock_cholesky_fn,
          _precomputed_divisor_matrix_cholesky=(
              k._precomputed_divisor_matrix_cholesky
          ),
      )
      y = np.random.uniform(-1, 1, size=(3, 7))
      z = np.random.uniform(-1, 1, size=(2, 7))
      self.assertAllClose(k.matrix(y, z), k2.matrix(y, z))
      mock_cholesky_fn.assert_not_called()
  
imports: import functools
  import itertools
  from unittest import mock
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import schur_complement
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 14 for calling the SchurComplementTest obj's underlying member method code with example usage: obj.testPrivateArgPreventsCholeskyRecomputation() without return types